Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Novel multistatic adaptive microwave imaging methods for early breast cancer detection
EURASIP Journal on Applied Signal Processing
Bilateral edge detection on a virtual hexagonal structure
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Object based similarity measure for breast medical image retrieval from data warehouse
Proceedings of the 2012 ACM Research in Applied Computation Symposium
SVM-based Harris corner detection for breast mammogram image normal/abnormal classification
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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About 1 in 8 women in the United States is expected to develop breast cancer over the course of herentire lifetime but a few medical imaging techniques have been applied for breast cancer screening. In addition, the feature extraction and comparison in medical images for breast cancer detection haverarely been reported in literature. We propose a new framework toextract agglomerated features in medical imagesand comparethem by relating original characteristic patterns thereof. Our method concentrates on three key aspects and they are: a comparison between intensity distributions of pixels collected by the hexagonal mask, detecting minimum gradient points in a radial intensity series, and generatinga characteristic pattern of the feature. The main contribution of ourproposed approach is improving a method of identifying features which is lesssensitive to noise in medical images for breast cancerdetectionand presenting an original design of relating features which is consistent to the orientation and size of the feature. Experimental results demonstrate that our proposed approach is more tolerant of image noise than prior research and generates an invariant characteristic pattern of various orientations and sizes.